Abstract
This study presents a planning model for utilizing emergency transmission capacity in the power system reserve market with renewable energy sources. To this end, first, the effects of the operation of a transmission line at higher power than rated power are described. The lifetime reduction of transmission lines caused by operation under these conditions is then measured, and finally, the price is determined based on the rate of lifetime reduction. This surplus capacity is then entered into a two-stage model of the energy and reserve market as a function of price offer, while also taking renewable energy sources into account. The numerical results of a 6-bus network indicates that the introduction of renewable energy sources reduced energy costs while increasing reserve market costs due to uncertainty. Despite the emergency capacity, such costs are reduced due to the network's utilization of low-cost resources.

Abstract
The use of machine learning in embedded systems is an interesting topic, especially with the growth in popularity of the Internet of Things (IoT). The capacity of a system, such as a robot, to self-localize, is a fundamental skill for its navigation and decision-making processes. This work focuses on the feasibility of using machine learning in a Raspberry Pi 4 Model B, solving the localization problem using images and fiducial markers (ArUco markers) in the context of the RobotAtFactory 4.0 competition. The approaches were validated using a realistically simulated scenario. Three algorithms were tested, and all were shown to be a good solution for a limited amount of data. Results also show that when the amount of data grows, only Multi-Layer Perception (MLP) is feasible for the embedded application due to the required training time and the resulting size of the model.

Abstract
The future is envisaged to have renewable energy resources replacing conventional sources of energy like fossil fuels. In this direction wind and solar energy is emerging to be a vital source of green energy. Although these resources are a promising aspect in providing clean and cheap electrical energy, one demerit is that it is intermittent and therefore unpredictable. This intermittent nature poses a challenge in maintaining the balance between generation and demand of electrical energy thus adversely affecting the system control. Also, the electrical energy companies involved in selling by participating in the electricity pool market need highly accurate solar and wind energy predictions for maximizing their profit. These issues demand a tool for accurate prediction of generation. This paper proposes machine learning prediction models for wind and solar irradiation. For this, a case study is done considering weather data of Malviya National Institute of Technology in Jaipur used to train the regression models. The best-trained model is tested with unseen data and shown to have reasonably good accuracy in predicting wind speed and solar irradiation. A comparative study of regression model performances is done. It is shown that Gaussian Process Regression-based prediction for solar irradiation and the Support Vector Machine outperforms the trained model for the wind speed predictions. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Abstract
Grid-Forming Power Inverters: Control and Applications is the first book dedicated to addressing the operation principles, grid codes, modeling, and control of grid-forming power inverters. The book initially discusses the need for this technology due to the substantial annual integration of inverter-based renewable energy resources. The key differences between the traditional grid-following and the emerging grid-forming inverter technologies are explained. Then, the book explores in detail various topics related to grid-forming power inverters, including requirements and grid standards, modeling, control, damping power system oscillations, dynamic stability under large fault events, virtual oscillator-controlled grid-forming inverters, grid-forming inverters interfacing battery energy storage, and islanded operation of grid-forming inverters. Features: • Explains the key differences between grid-following and grid-forming inverters • Explores the requirements and grid standards for grid-forming inverters • Provides detailed modeling of virtual synchronous generators • Explains various control strategies for grid-forming inverters • Investigates damping of power system oscillations using grid-forming converters • Elaborates on the dynamic stability of grid-forming inverters under large fault events • Focuses on practical applications

Abstract
The recent situation of the COVID-19 pandemic has stimulated both the discussion on the use of IT-related teaching tools and the exposure of the student population to vulnerabilities linked to cybersecurity literacy as an integral part of the educational projects of educational institutions and a component of the exercise of citizenship and social sustainability of educational communities. The study presented is based on the assumption that the use of gamification as an element or tool that promotes learning within digital environments may be feasible, and more specifically may function as a teaching element on issues related to cybersecurity for students, especially for higher education students. In order to quantify the openness of students to such a tool path, quantitative methodology was used, and a survey was carried out in two Polytechnic Institutions (PI), achieving a sample of 95 students, and seeking perceptions on positive impacts resulting from the creation of a game scenario for better learning. Results show that students, regardless of their higher education course, clearly understand what gamification is and its goals, and also that students adopt good cybersecurity practices according to their higher education course. This last result goes accordingly with the supposition that gamification can and should be used in cybersecurity literacy. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

Abstract
Point Set Registration (PSR) algorithms have very different underlying theoretical models to define a process that calculates the alignment solution between two point clouds. The selection of a particular PSR algorithm can be based on the efficiency (time to compute the alignment) and accuracy (a measure of error using the estimated alignment). In our specific context, previous work used a CPD algorithm to detect and quantify change in spatiotemporal datasets composed of moving and shape-changing objects represented by a sequence of time stamped 2D polygon boundaries. Though the results were promising, we question if the selection of a particular PSR algorithm influences the results of detection and quantification of change. In this work we review and compare several PSR algorithms, characterize test datasets and used metrics, and perform tests for the selected datasets. The results show pyCPD and cyCPD implementations of CPD to be good alternatives and that BCPD can have potential to be yet another alternative. The results also show that detection and quantification accuracy change for some of the tested PSR implementations.